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This study focuses on the current problems in earth system science (ESS), where machine learning (ML) algorithms can be applied. It provides an overview of previous studies, ongoing work at the Ministry of Earth Sciences, Government of India, and future applications of ML algorithms to some significant earth science problems. We compare previous studies, a mind map of multidimensional areas related to ML and Gartner's hype cycle for ML in ESS. We mainly focus on the critical components in earth sciences, including studies on the atmosphere, oceans, biosphere, hydrogeology, human health and seismology. Various artificial intelligence (AI)/ML applications to problems in the core fields of earth sciences are discussed, in addition to gap areas and the potential for AI techniques. [ABSTRACT FROM AUTHOR]

The article focuses on prospective applications of artificial intelligence/machine (AI/ML) learning techniques in earth sciences. Topics include the application of AI/ML to earth sciences problems has conducted, the main aim of the meeting was to gather researchers working on AI/ML and scientists from different areas of earth sciences to exchange knowledge, and the delegates has shared their opinions about prospective work has pursued on topics has related to earth sciences.

The article discusses workshop on cloud dynamics, micro-physics and small-scale simulations held at the Indian Institute of Tropical Meteorology in Pune, India from August 13-17, 2018. It is mentioned that Workshop aims to bring together scientists and students to exchange ideas and improve our understanding on the physics of clouds and their numerical simulations. The article adds scientists including W. W. Grabowski, Suryachandra A. Rao, and David Richter presented various papers.

Access, efficiency and affordability of agricultural information are a prerequisite for achieving set targets of agricultural productivity. Information and communication technology (ICT) equipped with social media reach can play a leading role in disseminating correct information to needful farmers at the right time. The ICAR-Indian Agricultural Research Institute in-house initiative 'Pusa Samachar' is an innovative multimediabased extension advisory model that targets to reach farmers across India with timely, location-specific and customized farm information. The present study was conducted to get an overall idea about the viewership pattern and to validate this model under content, design, ease of understanding and fulfilment of information needs. Analysis of secondary data from YouTube analytics and primary data collected from different stakeholders has shown that with changes in the format, style and presentation of the content, the trend of viewing changed and therefore four episodes performed better than the others with respect to the number of views, watch hours and subscribers added per episode. The findings also indicate that the number of views depend on the episode duration (2 = 83.049, P = 0.001264); however, the average view duration per episode is independent of episode duration (2 = 3.1821, P = 1). Overall, the present study has shown how initiatives like Pusa Samachar have immense potential to reach farmers across the nation through social media. Such initiatives can be taken up by other public institutions as reliability and validity of their content is high. However, the results have shown that diversification with respect to content and audio-visuals is further needed to attract and retain more audience. [ABSTRACT FROM AUTHOR]